Electrochemical in-biosensing computing
- 1State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry, Nanjing University , Nanjing 210023 ,
Abstract
Artificial intelligence (AI)-aided electrochemical biosensing is becoming integral parts in numerous scenarios. However, existing systems generally perform algorithms in external signal processing units. The necessity of analog-to-digital conversion and data transfer results in high complexity, low working efficiency and concern of privacy. In-sensor computing has made great progress in perceiving and processing physical signals, which, nevertheless, faces inherent restriction in biochemical scenarios due to the lack of aqueous compatibility and the necessity of an array. Here, we realized neuromorphic electrochemical in-biosensing computing using just a single photoelectrochemical transistor, which can itself not only perform multi-target biosensing but also constitute a single-layer algorithmic classifier. It is based on a rationally designed multi-gate photoelectrochemical transistor, whose architecture and synaptic memory enable built-in vector-matrix multiplication and light-tunable responsivity. The proof-of-concept is demonstrated by simultaneous sensing and classification of biomarker microRNA fingerprints in real biological samples, which opens the possibilities for next-generation AI-driven electrochemical biosensing with edge computing ability.
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BibTeX
@article{yuan2026electrochemical,
title = {{Electrochemical in-biosensing computing}},
author = {Cheng Yuan and Ao Xiao and Shuang Wu and Xing‐Shi Liu and Jing‐Juan Xu and H T Chen and Weiwei Zhao},
journal = {National Science Review},
year = {2026},
month = may,
doi = {10.1093/nsr/nwag290},
url = {https://doi.org/10.1093/nsr/nwag290},
note = {CC BY},
}
RIS
TY - JOUR
TI - Electrochemical in-biosensing computing
AU - Cheng Yuan
AU - Ao Xiao
AU - Shuang Wu
AU - Xing‐Shi Liu
AU - Jing‐Juan Xu
AU - H T Chen
AU - Weiwei Zhao
PY - 2026
DA - 2026/05/16
JO - National Science Review
DO - 10.1093/nsr/nwag290
UR - https://doi.org/10.1093/nsr/nwag290
AB - Artificial intelligence (AI)-aided electrochemical biosensing is becoming integral parts in numerous scenarios. However, existing systems generally perform algorithms in external signal processing units. The necessity of analog-to-digital conversion and data transfer results in high complexity, low working efficiency and concern of privacy. In-sensor computing has made great progress in perceiving and processing physical signals, which, nevertheless, faces inherent restriction in biochemical scenarios due to the lack of aqueous compatibility and the necessity of an array. Here, we realized neuromorphic electrochemical in-biosensing computing using just a single photoelectrochemical transistor, which can itself not only perform multi-target biosensing but also constitute a single-layer algorithmic classifier. It is based on a rationally designed multi-gate photoelectrochemical transistor, whose architecture and synaptic memory enable built-in vector-matrix multiplication and light-tunable responsivity. The proof-of-concept is demonstrated by simultaneous sensing and classification of biomarker microRNA fingerprints in real biological samples, which opens the possibilities for next-generation AI-driven electrochemical biosensing with edge computing ability.
SN - 2053-714X
SN - 2095-5138
ER -